Identification of fuzzy prediction models through hyperellipsoidal clustering

Abstract

To build a fuzzy model, as proposed by Takagi and Sugeno (1985), the authors emphasize an interactive approach in which knowledge or intuition can play an important role. It is impossible in principle, due to the nature of the data, to specify a criterion and procedure to obtain an ideal fuzzy model. The main subject of fuzzy modeling is how to analyze data in order to summarize it to a certain extent so that one can judge the quality of a model by intuition. The main proposal in this paper is a clustering technique which takes into account both continuity and linearity of the data distribution. The authors call this technique the hyperellipsoidal clustering method, which assists modelers in finding fuzzy subsets suitable for building a fuzzy model. The authors deal with other problems in fuzzy modeling as well, such as the effect of data standardization, the selection of conditional and explanatory variables, the shape of a membership function and its tuning problem, the manner of evaluating weights of rules, and the simulation technique for verifying a fuzzy model